Detecting irregularities on a device

ABSTRACT

A system and method for the detection of irregularities, such as fraud or malware, running on a device, is disclosed. An example method includes receiving new ones of data items indicative of the device&#39;s current operation; determining whether the new ones of data items deviate from the device&#39;s typical operation by comparing the new ones of data items to a profile relating to the typical operation of the device, wherein the deviating includes either using an infrequently used one of incoming ports and outgoing ports or continually accessing a new website. The example method can further include based on the determining: updating the device baseline profile to create an updated device baseline profile with the new ones of data items if the new ones of data items do not deviate from the typical operation of the device; and generating an alert if the new ones of data items do deviate from the typical operation of the device.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. patent applicationSer. No. 15/669,698, filed Aug. 4, 2017, (now issued as U.S. Pat. No.10,558,799), which is a continuation of U.S. patent application Ser. No.14/484,633, filed Sep. 12, 2014 (now issued as U.S. Pat. No. 9,767,278),which claims priority to and the benefit of the filing date of BritishPatent Application No. GB1316319.1. This application is related to U.S.patent application Ser. No. 12/965,226, filed Dec. 10, 2010 (now issuedas U.S. Pat. No. 8,543,689). The foregoing applications are herebyincorporated by reference in their entirety.

FIELD OF THE PRESENT TECHNOLOGY

This application relates to an apparatus and a method for detection ofirregularities on a device, such irregularities include but are notlimited to malware or fraud.

BACKGROUND

The term “malware” is short for “a malicious software” and is softwarethat is used or programmed to disrupt operation of an individualcomputer and/or computer network, to gather sensitive information or togain access to private computer systems. The malware can appear in theform of code, scripts, active content, and other software. The malwareincludes, but is not limited to, computer viruses, ransomware, worms,Trojan horses, rootkits, keyloggers, dialers, spyware, outware, rocksecurity software. The majority of active malware threats are usuallyworms or Trojans, rather than viruses.

As attacks by malware become more frequent, programs and methods havebeen developed specifically to combat the malware. One commonly usedapproach is to install a scanner onto a user's computer, which hooksdeep into the operating system and functions in a manner similar to theway in which the malware itself would attempt to operate. The scanner,on attempted access of a file, checks if the accessed file is alegitimate file, or not. The access operation would be stopped if thefile is considered to be malware by the scanner and the file will bedealt with by the scanner in a pre-defined way. A user will generally benotified. This approach may considerably slow down speed of operationthe operating system and depends on the effectiveness of the scanner.

Another approach to combatting malware is to attempt to providereal-type protection against the installation of the malware on theuser's computer. This approach scans the incoming network data for amalware and blocks any threats identified.

Empty-malware software programs can be used for detection and removal ofthe malware that has already been installed onto computer. This approachscans the contents of the operating system registry, operating systemfiles and installed computer programs on the user's computer andprovides a list of any identified threats, allowing the user to choosewhich ones of the files to delete or keep, or to compare this list to alist of known malware and removing the related files.

Typically, malware products detect the malware based on heuristics or onsignatures. Other malware products maintain a black list and/or a whitelist of files that are known to be related to the malware.

Methods of detecting malware using a plurality of detection sources todetect potential attacks of malware are known. The use of more than onedetection source enables a more reliable decision to be made aboutwhether a computer network is under attack. For example, US patentapplication publication No. US 2006/0259967 (Thomas et al.) teaches amethod for determining whether a network is under attack by sharing datafrom several event detection systems and passing the suspicious eventdata to a centralized location for analysis. The suspicious event datais generated in an event valuation computer including an evaluationcomponent. The evaluation component analyses the suspicious eventsobserved in the network and quantifies the likelihood that the networkis infected or under attack by malware. The evaluation component can, inone aspect of the disclosure, determine whether the number of suspiciousevents in a given timeframe is higher than a predetermined threshold.The evaluation component may also analyze metadata generated by theevent detection systems and thereby calculate a suspicious scorerepresenting the probability that the network is infected or underattack.

US patent application publication No. 2008/0141371 (Bradicich et al.)discloses a method and system for heuristic malware detection. Thedetection method includes merging a baseline inventory of fileattributes for a number of files from each client computing system. Themethod includes receipt of an updated inventory of file attributes in acurrent inventory survey from different ones of the clients. Eachreceived inventory survey can be compared to the merged inventory and,in response to the comparison, a deviant pattern or file attributechanges can be detected in at least one inventory survey for acorresponding one of the clients. The deviant pattern can be classifiedas one of a benign event or a malware attack.

Similarly, a thesis by Blount entitled “Adaptive rule-based malwaredetection employing learning classifier systems”, Missouri University ofScience and Technology, 2011, discloses a rule-based expert system forthe detection of malware with an evolutionary learning algorithm. Thiscreates a self-training adaptive malware detection system thatdynamically evolves detection rules. The thesis uses a training set totrain the algorithm.

SUMMARY

A system for the detection of irregularities of a device is taught inthis specification. This system comprises a monitoring program forreviewing data relating to operation of the device, a device profileincluding data items relating to typical operation of the device; and analert module for generating an alert on detection of irregularityrelating to the device.

The data items comprise at least one of ports associated with processes,addresses of connectable devices, volumes of data and the irregularitiesare one or more of malware or fraud.

A method for the detection of irregularities of a device is also taught.The method comprises detecting a plurality of data items relating to theoperation of the device, comparing the detected plurality of data itemswith a device profile, and generating an alert on detection ofirregularities.

The method can also comprise updating the device profile by monitoringthe data items over a period of time and generating data items forstorage in the device profile.

BRIEF DESCRIPTION OF THE FIGURES

For a more complete understanding of the present invention and theadvantages thereof, reference is now made to the following descriptionand the accompanying drawings, in which:

FIG. 1 shows an overview of a user computer connected to a network.

FIG. 2 shows an example of messages generated by the user computer.

FIG. 3 shows an example of connections to services

FIG. 4 shows a flow diagram of the method.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

The invention will now be described on the basis of the drawings. Itwill be understood that the embodiments and aspects of the inventiondescribed herein are only examples and do not limit the protective scopeof the claims in any way. The invention is defined by the claims andtheir equivalents. It will be understood that features of one aspect orembodiment of the invention can be combined with a feature of adifferent aspect or aspects and/or embodiments of the invention.

FIG. 1 shows a user computer 10 with a plurality of outgoing connections26 and a plurality of incoming connections 30 in a computer network 100.The outgoing connections 26 and the incoming connections 30 areconnected to one or more servers 15 a-c using, for example, a TCPprotocol. A plurality of processes 40 a-c are running on the usercomputer 10. The processes 40 a-c include regular processes, such as,but not limited to a Splunk process 40 a, a Python process 40 b, and amaster process 40 c. Each one of the regular processes 40 a-c will useone or more of the ports of the outgoing ports 25 or the incoming ports35. The typical port number is shown in the schematic boxes illustratingthe processes 40 a-c.

A malware 50 may be operating on the user's computer 10. The malware 50could be a specially developed piece of software code or could be aregular piece of code and will generally also run as a process. Themalware 50 is also connected to one or more of the outgoing ports 25 orthe incoming ports 35. In the aspect of the invention shown in FIG. 1,it is assumed that the malware 50 is a process in the user computer 10running a file transfer protocol using the outgoing port 41217. Themalware could also be a modified version of an existing piece of code.

A monitoring program 60 installed within the network 100 in which theuser computer 10 is operating continually monitors the network 100 andthe user computer 10 as well as messages 70 exchanged within the network100 and/or generated by the user computer 10. The monitoring program 60uses a variety of data sources for performing the monitoring.

The monitoring program 60 uses data sources based on network flowtraffic statistics through the computer network 100. These data sourcesinclude proxy logs and NetFlow records, which record the destination ofdata sent through the outgoing ports 25 and the source of incoming datareceived through the incoming ports 35. The monitoring program 60analyzes headers in the data records and can also investigate whichbrowsers are being run on the user computer 10.

Many computer networks 100 also have a DNS server 110 located in theprivate network, as well as having access to public DNS servers. The DNSserver 110 includes a variety of data log entries, including timestamps, indicating which ones of the user computers 10 attempted toaccess which web sites or external servers at which period of time.

The monitoring program 60 can also review headers in emails and/or othermessages 70 sent throughout the computer network 100. The email headerswill include information, such as the time, the destination and thesource, as well as having information about the size of the email.

It will be appreciated that these data sources are merely exemplary andthat other data sources can be used or supplied. Only a single usercomputer 10 is shown in FIG. 1 for simplicity. In practice, there willbe a large number of user computers 10 and servers 15 a-c. It will beappreciated that the network 100 may also contain other devices that cangenerate messages 70 or other data.

The monitoring program 60 creates a user profile 62, stored in a userprofile database 65 attached to the monitoring program 60, for each oneof the user computers 10 using the plurality of data sources. It will beappreciated that the user profile database 65 contains more than oneuser profile 62. The user profile 62 in the user profile database 65receives data items 66, that indicate how the user computer 10 generallyreacts with the network 100 as well as with servers 15 a-c and otherdevices in the network 100. For example, the user profile 62 identifieswhich ones of the outgoing ports 25 and the incoming ports 35 aretypically used by the user computer 10 for which processes 40. The userprofile 62 will continually be updated as new ones of the data items 66relating to activity of the user computer 10 are generated. The userprofile 62 creates in essence a baseline from which the ‘normal’ can bededuced.

Suppose now that the malware process 50 starts on the user computer 10.The monitoring program 60 will receive further data items that indicatethat behavior of the user computer 10 deviates from the behaviourexpected by comparison the user profile 62 stored in the user profiledatabase 65. Non limiting examples of such deviant behaviour includemassive amounts of data being transferred to one of the servers 15 a-c,or continual access to a new website. The monitoring program 60 cannotify an administrator of a possible malware infection of the usercomputer and the administrator can investigate the user computer 10.

An example is shown in FIG. 2. FIG. 2 shows how the monitoring systemreports anomalous behaviour. On Thursday October 2012, an ftp process 40started on a server 15 a-c. This process 40 was unusual compared to thenormal network processes 40 a-c running on this server. The monitoringprogram 60 automatically identified this and reported this as a non-zeroanomaly score.

Another example is shown in FIG. 3. in which two user computers 10connected to a server 15 running a plurality of processes 40. One of theprocesses 40 m connects generally to the same IP address 10.135.1.7.However in one instance this connects to an IP address 10.230.80.46(shown in the example in FIG. 3 as 10.230.80.x), which is unexpected.This unexpected connection will be picked up by the monitoring program60 and reported to the administrator.

The monitoring program 60 can also review attempts to connect to theuser computer 10 through various ones of the incoming ports 35. Forexample, incoming requests for a particular process 40 a-c would beexpected on several ones of the incoming ports 35. An attempt to connectto a particular process 40 would be detected by the monitoring program60 and indicated to the administrator. The monitoring program 60 in thisexample would identify that a connection to a particular process througha particular port 25 has never or rarely been seen is a deviantbehaviour and generate via alert module 67, an alert 80 for theadministrator.

FIG. 4 shows an outline of a process 400 for the detection ofirregularities, such as malware. The process 400 starts at 410 and instep 420 data from the various data sources is gathered. In step 430 thegathered data is compared with one or more of the user profiles 62 and,if an anomaly is discovered, an alert is generated in step 440 such thatthe administrator can investigate in step 450. In step 460, the userprofile 62 is updated from the newly gathered data items. The userprofile 62 will also be updated using data relating to the anomaly.

The updating of the user profile 62 in step 460 ensures that the userprofile 62 is continually adapted to new devices or other computersinserted into the computer network 100 and/or changes to the processes40 a-c running on the user computer.

In a further aspect of the invention, the system and method can be usedto detect other irregularities on the user computer 10 or in thecomputer network 100. It would be possible, for example, to use theteachings of a disclosure to detect fraud by users of the user computer10. The fraud can be detected by, for example, identifying anomalousattempts to access certain websites, which are not normally accessed,or, by an attempt to transfer significant amounts of data to a computeror memory device that is not normally in use, or by the generation of alarge number of emails in a particular period of time.

The detection of fraud is made by detection of unusual activity in theuser profile 62. One method for identifying fraud is by comparing thedifferent ones of the user profiles 62 of different users of thecomputer. If one of the user profiles 62 is substantially different thanother ones of the user profiles 62, then notification can be made to anadministrator or a fraud officer to investigate the user and the usercomputer 10. Another method for identifying fraud is if the user profile62 suddenly changes.

The foregoing description of the preferred embodiment of the inventionhas been presented for purposes of illustration and description. It isnot intended to be exhaustive or to limit the invention to the preciseform disclosed, and modifications and variations are possible in lightof the above teachings or may be acquired from practice of theinvention. The embodiment was chosen and described in order to explainthe principles of the invention and its practical application to enableone skilled in the art to utilize the invention in various embodimentsas are suited to the particular use contemplated. It is intended thatthe scope of the invention be defined by the claims appended hereto, andtheir equivalents. The entirety of each of the aforementioned documentsis incorporated by reference herein.

What is claimed is:
 1. A method for detection of irregularities of adevice in a network, the method comprising: receiving new ones of dataitems indicative of a current operation of a device; determining whetherthe new ones of data items deviate from typical operation of the deviceby comparing the new ones of data items to a profile relating to thetypical operation of the device, the profile comprising: (i) incomingports associated with processes, (ii) outgoing ports associated withprocesses; and (iii) Internet Protocol (IP) addresses associated withprocesses, wherein the deviating from the typical operation of thedevice includes either using an infrequently used one of the incomingports and the outgoing ports or continually accessing a new website;based on the determining, updating the device baseline profile to createan updated device baseline profile with the new ones of data items ifthe new ones of data items do not deviate from the typical operation ofthe device; and based on the determining, generating an alert if the newones of data items do deviate from the typical operation of the device.2. The method of claim 1, further comprising: detecting a plurality ofthe data items relating to the typical operation of the device; andcreating the device baseline profile including the plurality of the dataitems relating to the typical operation of the device, the plurality ofthe data items comprising: (i) the incoming ports associated withprocesses, (ii) the outgoing ports associated with the processes, and(iii) the IP addresses associated with the processes.
 3. The method ofclaim 1, wherein the deviating from the typical operation of the devicefurther includes transferring unusual amounts of data.
 4. The method ofclaim 1, wherein the deviating from the typical operation of the devicefurther includes connecting to an unexpected one of the IP addresses. 5.The method of claim 1, wherein the deviating from the typical operationof the device further includes using an infrequently used one of theincoming ports and the outgoing ports.
 6. The method of claim 1, whereinthe irregularities comprise malware.
 7. The method of claim 1, whereinthe irregularities comprise fraud.
 8. The method of claim 1, furthercomprising analyzing headers in the data items and headers in emailmessages sent through the network.
 9. The method of claim 1, whereindata sources for the data items are based on network flow trafficstatistics through the network.
 10. The method of claim 1, wherein datasources for the data items include proxy logs and NetFlow records whichrecord a destination of data sent through the outgoing ports and recorda source of data received through the incoming ports.
 11. A system fordetection of irregularities of a device, the system comprising: ahardware processor; and a memory communicatively coupled with thehardware processor, the memory storing instructions which when executedby the hardware processor performs a method, the method comprising:receiving new ones of data items indicative of a current operation of adevice; determining whether the new ones of data items deviate fromtypical operation of the device by comparing the new ones of data itemsto a profile relating to the typical operation of the device, theprofile comprising: (i) incoming ports associated with processes, (ii)outgoing ports associated with processes; and (iii) Internet Protocol(IP) addresses associated with processes, wherein the deviating from thetypical operation of the device includes either using an infrequentlyused one of the incoming ports and the outgoing ports or continuallyaccessing a new website; based on the determining, updating the devicebaseline profile to create an updated device baseline profile with thenew ones of data items if the new ones of data items do not deviate fromthe typical operation of the device; and based on the determining,generating an alert if the new ones of data items do deviate from thetypical operation of the device.
 12. The system of claim 11, wherein thedeviating from the typical operation of the device further includestransferring unusual amounts of data.
 13. The system of claim 11,wherein the deviating from the typical operation of the device furtherincludes connecting to an unexpected one of the IP addresses.
 14. Thesystem of claim 11, wherein the deviating from the typical operation ofthe device further includes using an infrequently used one of theincoming ports and the outgoing ports.
 15. The system of claim 11,wherein the method further comprises: detecting a plurality of the dataitems relating to the typical operation of the device; and creating thedevice baseline profile including the plurality of the data itemsrelating to the typical operation of the device, the plurality of thedata items comprising: (i) the incoming ports associated with processes,(ii) the outgoing ports associated with the processes, and (iii) the IPaddresses associated with the processes.
 16. A method for detection ofirregularities of a device, the method comprising: receiving new ones ofdata items indicative of a current operation of a device on a network;determining whether the new ones of data items deviate from typicaloperation of the device by comparing the new ones of data items to aprofile relating to the typical operation of the device, the profilecomprising: (i) incoming ports associated with processes, (ii) outgoingports associated with processes; and (iii) Internet Protocol (IP)addresses associated with processes, wherein the deviating from thetypical operation of the device includes using an infrequently used oneof the incoming ports and the outgoing ports, continually accessing anew website and transferring unusual amounts of data, or connecting toan unexpected IP address and transferring unusual amounts of data; basedon the determining, updating the device baseline profile to create anupdated device baseline profile with the new ones of data items if thenew ones of data items do not deviate from the typical operation of thedevice; and based on the determining, generating an alert if the newones of data items do deviate from the typical operation of the device.17. The method of claim 16, wherein data sources for the data itemsinclude proxy logs and/or NetFlow records which record a destination ofdata sent through the outgoing ports and record a source of datareceived through the incoming ports.
 18. The method of claim 16, whereinthe irregularities comprise malware or fraud.